Autism Spectrum Disorder and Attention-Deficit/Hyperactivity Disorder: Shared or Unique Neurocognitive Profiles?
Bibliographic record
Abstract
Attention-deficit/hyperactivity (ADHD) and autism spectrum (ASD) disorders are commonly co-occurring conditions characterized by neurocognitive impairments. Few studies have directly compared neurocognitive profiles in ADHD and ASD and fewer still have controlled for comorbidity of ADHD and ASD. All direct comparisons have been in clinic samples, leaving the question of generalizability of results unaddressed. We compared neurocognitive performance in clinically ascertained ASD (n = 261) and ADHD (n = 423) cases and controls (n = 162), 6.0-17.9 years of age. We also compared ASD (n = 190) and ADHD (n = 926) cases ascertained in the community with controls (n = 14,842) of similar age. Using the stop-signal task (SST), we measured response inhibition (stop-signal reaction time-SSRT), sustained attention (defined as reaction time variability-RTV), and reaction time (RT). We controlled for comorbidity using ADHD and ASD trait scores and categorically-defined ADHD. Compared with controls, both clinic ADHD and ASD had significantly longer SSRT and RTV than controls and did not differ from each other. ADHD traits accounted for neurocognitive impairment in ASD, but not vice versa. There were no group differences for RT. Similar patterns of neurocognitive impairment were observed in the community sample. In the largest direct comparison of ADHD and ASD to date, we found impaired response inhibition and sustained attention in both disorders. However, neurocognitive impairment in ASD was almost completely accounted for by comorbid ADHD. Results generalized in the community sample indicating that referral bias alone did not drive results. Response inhibition and sustained attention likely play a role in ADHD and ASD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".